Apparatuses and methods for facilitating an insertion of markers in content

ABSTRACT

Aspects of the subject disclosure may include, for example, applying first data associated with a first content item to a model to generate first classification characteristics, analyzing the first classification characteristics to generate a first marker, wherein the first marker delineates a first location of inventory within the first content item, selecting a first creative to populate a portion of the inventory, and populating, based on the selecting, the portion of the inventory with the first creative. Other embodiments are disclosed.

CROSS-REFERENCE TO RELATED APPLICATION(S)

The instant application is a continuation of U.S. patent applicationSer. No. 17/319,153, filed on May 13, 2021. All sections of theaforementioned application(s) are incorporated herein by reference intheir entirety.

FIELD OF THE DISCLOSURE

The subject disclosure relates to apparatuses and methods forfacilitating an insertion of markers in content.

BACKGROUND

As the world increasingly becomes connected via vast communicationnetworks and via various communication devices, additional opportunitiesare created/generated to provision data to such devices. For, such datamay pertain to content (e.g., media) that may be presented to one ormore users.

In many instances, a creation and distribution of content is subsidizedvia a use of one or more advertisements that are presented duringadvertising timeslots/breaks in the content. The advertisements help todefray the costs associated with creating and distributing the contentand are used to populate inventory within the content. Typically, acontent item is processed per a specification to identify a total lengthof the content item. Thereafter, an advertising opportunityspecification is established that indicates a count of advertisementsthat are to be inserted into the content item (where the count is basedon the total length of the content item). Markers are generated anddistributed throughout the content item at various points (where themarkers may delineate the start and end of an advertising timeslot/breakassociated with the inventory). One or more advertisements are selectedfor presentation in conjunction with the advertising timeslot/break topopulate the inventory.

While the use of markers as set forth above is effective in terms ofdelineating inventory within content items, in many instances themarkers are inserted at points/locations within a content item that areunnatural/awkward. For example, and in the context of a content itemincluding a video of a speech, a marker may be inserted at apoint/location in the video where the speaker is mid-sentence. Markerslocated at unnatural or improper points/locations in a content item maytend to have a negative impact on the quality of the user experience(where the user in this context is the person consuming the presentationof the content item). The erosion/degradation of the quality of the userexperience may cause the user to consume less of the content item (orrelated content items), which, in turn, may have a negative impact onthe performance (e.g., revenue, profit, etc.) of the creator ordistributor of the content item(s).

BRIEF DESCRIPTION OF THE DRAWINGS

Reference will now be made to the accompanying drawings, which are notnecessarily drawn to scale, and wherein:

FIG. 1 is a block diagram illustrating an exemplary, non-limitingembodiment of a communications network in accordance with variousaspects described herein.

FIG. 2A is a block diagram illustrating an example, non-limitingembodiment of a system functioning within the communication network ofFIG. 1 for training a model for use with video in accordance withvarious aspects described herein.

FIG. 2B is a block diagram illustrating an example, non-limitingembodiment of a system functioning within the communication network ofFIG. 1 for training a model for use with audio in accordance withvarious aspects described herein.

FIG. 2C is a block diagram illustrating an example, non-limitingembodiment of a system functioning within the communication network ofFIG. 1 for classifying segments of content and tagging the classifiedsegments in accordance with various aspects described herein.

FIG. 2D depicts an illustrative embodiment of a method in accordancewith various aspects described herein.

FIG. 3 is a block diagram illustrating an example, non-limitingembodiment of a virtualized communication network in accordance withvarious aspects described herein.

FIG. 4 is a block diagram of an example, non-limiting embodiment of acomputing environment in accordance with various aspects describedherein.

FIG. 5 is a block diagram of an example, non-limiting embodiment of amobile network platform in accordance with various aspects describedherein.

FIG. 6 is a block diagram of an example, non-limiting embodiment of acommunication device in accordance with various aspects describedherein.

DETAILED DESCRIPTION

The subject disclosure describes, among other things, illustrativeembodiments for identifying locations for inventory within contentitems. Other embodiments are described in the subject disclosure.

One or more aspects of the subject disclosure include, in whole or inpart, obtaining a model, obtaining first data corresponding to a firstcontent item, sampling the first data to obtain first samples of thefirst data, applying the first samples to the model to generate firstclassification characteristics, analyzing the first classificationcharacteristics to generate a first plurality of markers, wherein eachof the first plurality of markers delineates respective inventory withinthe first content item, selecting creatives to populate the inventorywithin the first content item, resulting in first selected creatives,and causing the inventory within the first content item to be populatedwith the first selected creatives.

One or more aspects of the subject disclosure include, in whole or inpart, obtaining first data corresponding to a first content item,applying the first data to a model to generate first classificationcharacteristics, analyzing the first classification characteristics togenerate a first plurality of markers, wherein each of the firstplurality of markers delineates respective first locations of inventorywithin the first content item, selecting creatives to populate theinventory within the first content item, resulting in first selectedcreatives, and causing the inventory within the first content item to bepopulated with the first selected creatives.

One or more aspects of the subject disclosure include, in whole or inpart, applying first data associated with a first content item to amodel to generate first classification characteristics, analyzing thefirst classification characteristics to generate a first marker, whereinthe first marker delineates a first location of inventory within thefirst content item, selecting a first creative to populate a portion ofthe inventory, and populating, based on the selecting, the inventorywith the first creative.

Referring now to FIG. 1 , a block diagram is shown illustrating anexample, non-limiting embodiment of a system 100 in accordance withvarious aspects described herein. For example, system 100 can facilitatein whole or in part obtaining a model, obtaining first datacorresponding to a first content item, sampling the first data to obtainfirst samples of the first data, applying the first samples to the modelto generate first classification characteristics, analyzing the firstclassification characteristics to generate a first plurality of markers,wherein each of the first plurality of markers delineates respectiveinventory within the first content item, selecting creatives to populatethe inventory within the first content item, resulting in first selectedcreatives, and causing the inventory within the first content item to bepopulated with the first selected creatives. System 100 can facilitatein whole or in part obtaining first data corresponding to a firstcontent item, applying the first data to a model to generate firstclassification characteristics, analyzing the first classificationcharacteristics to generate a first plurality of markers, wherein eachof the first plurality of markers delineates respective first locationsof inventory within the first content item, selecting creatives topopulate the inventory within the first content item, resulting in firstselected creatives, and causing the inventory within the first contentitem to be populated with the first selected creatives. System 100 canfacilitate in whole or in part applying first data associated with afirst content item to a model to generate first classificationcharacteristics, analyzing the first classification characteristics togenerate a first marker, wherein the first marker delineates a firstlocation of inventory within the first content item, selecting a firstcreative to populate a portion of the inventory, and populating, basedon the selecting, the inventory with the first creative.

In particular, in FIG. 1 a communications network 125 is presented forproviding broadband access 110 to a plurality of data terminals 114 viaaccess terminal 112, wireless access 120 to a plurality of mobiledevices 124 and vehicle 126 via base station or access point 122, voiceaccess 130 to a plurality of telephony devices 134, via switching device132 and/or media access 140 to a plurality of audio/video displaydevices 144 via media terminal 142. In addition, communication network125 is coupled to one or more content sources 175 of audio, video,graphics, text and/or other media. While broadband access 110, wirelessaccess 120, voice access 130 and media access 140 are shown separately,one or more of these forms of access can be combined to provide multipleaccess services to a single client device (e.g., mobile devices 124 canreceive media content via media terminal 142, data terminal 114 can beprovided voice access via switching device 132, and so on).

The communications network 125 includes a plurality of network elements(NE) 150, 152, 154, 156, etc. for facilitating the broadband access 110,wireless access 120, voice access 130, media access 140 and/or thedistribution of content from content sources 175. The communicationsnetwork 125 can include a circuit switched or packet switched network, avoice over Internet protocol (VoIP) network, Internet protocol (IP)network, a cable network, a passive or active optical network, a 4G, 5G,or higher generation wireless access network, WIMAX network,UltraWideband network, personal area network or other wireless accessnetwork, a broadcast satellite network and/or other communicationsnetwork.

In various embodiments, the access terminal 112 can include a digitalsubscriber line access multiplexer (DSLAM), cable modem terminationsystem (CMTS), optical line terminal (OLT) and/or other access terminal.The data terminals 114 can include personal computers, laptop computers,netbook computers, tablets or other computing devices along with digitalsubscriber line (DSL) modems, data over coax service interfacespecification (DOCSIS) modems or other cable modems, a wireless modemsuch as a 4G, 5G, or higher generation modem, an optical modem and/orother access devices.

In various embodiments, the base station or access point 122 can includea 4G, 5G, or higher generation base station, an access point thatoperates via an 802.11 standard such as 802.11n, 802.11ac or otherwireless access terminal. The mobile devices 124 can include mobilephones, e-readers, tablets, phablets, wireless modems, and/or othermobile computing devices.

In various embodiments, the switching device 132 can include a privatebranch exchange or central office switch, a media services gateway, VoIPgateway or other gateway device and/or other switching device. Thetelephony devices 134 can include traditional telephones (with orwithout a terminal adapter), VoIP telephones and/or other telephonydevices.

In various embodiments, the media terminal 142 can include a cablehead-end or other TV head-end, a satellite receiver, gateway or othermedia terminal 142. The display devices 144 can include televisions withor without a set top box, personal computers and/or other displaydevices.

In various embodiments, the content sources 175 include broadcasttelevision and radio sources, video on demand platforms and streamingvideo and audio services platforms, one or more content data networks,data servers, web servers and other content servers, and/or othersources of media.

In various embodiments, the communications network 125 can includewired, optical and/or wireless links and the network elements 150, 152,154, 156, etc. can include service switching points, signal transferpoints, service control points, network gateways, media distributionhubs, servers, firewalls, routers, edge devices, switches and othernetwork nodes for routing and controlling communications traffic overwired, optical and wireless links as part of the Internet and otherpublic networks as well as one or more private networks, for managingsubscriber access, for billing and network management and for supportingother network functions.

FIG. 2A is a block diagram illustrating an example, non-limitingembodiment of a system 200 a functioning within the communicationnetwork 100 of FIG. 1 in accordance with various aspects describedherein. The system 200 a may be utilized as part of a trainingalgorithm/routine to generate classifications of one or more portions ofcontent/content items, such as for example video images. Thoseclassifications may be used for establishing/generating markers for aninsertion of one or more creatives as described in further detail below.

In operation, the system 200 a may obtain as input raw image or videoframes 202 a. The frames 202 a may correspond to images or videogenerated by a production studio. In some embodiments, the frames 202 amay be sourced or obtained from a user equipment (e.g., a mobile phone).In some embodiments, the frames 202 a may correspond to or include astreaming or over-the-top (OTT) video. In some embodiments, the frames202 a may be uploaded to one or more devices (e.g., one or more servers)to facilitate a subsequent download to one or more devices. In someembodiments, the frames 202 a may correspond to live content, such asfor example content distributed over one or more platforms (e.g., viaone or more social media platforms).

The frames 202 a may be provided to a processor, such as for example asampling processor 206 a as shown in FIG. 2A. The sampling processor 206a may sample the frames 202 a to generate sample frames 210 a. In someembodiments, a sample rate that is used by the sampling processor 206 ato sample the frames 202 a may be based on an identification of acontent item (e.g., images, video, or a combination thereof) associatedwith the frames 202 a. For example, if the content item is known to bean action movie, then a high sampling rate may be used. Conversely, ifthe content item is known to be heavy on dialogue and limited/low interms of action, a lower sampling rate may be used. In some embodiments,the sampling rate may be based in part on an availability of resources.For example, if an amount of processing resources (e.g., resourcesprovided by the sampling processor 206 a) that are available is high, agreater number of sample frames 210 a may be generated relative to astate/condition where the amount of processing resources that areavailable is low. In some embodiments, a variable sampling rate may beused, which is to say that a sample rate that is used may be modifiedover time. For example, a first sample rate may be used for a firstportion of the frames 202 a and a second sample rate may be used for asecond portion of the frames 202 a, where the second sample rate may bedifferent from the first sample rate.

The sampled frames 210 a may be provided as an input to a classificationprocessor 218 a (where the classification processor 218 a may correspondto a same entity/device as the sampling processor 206 a in someembodiments). To the extent that training data 214 a is available, thetraining data 214 a may also be provided as an input to theclassification processor 218 a.

The training data 214 a may represent a corpus of data that isrepresentative of a cross-section of content items (e.g., images orvideos) that may be analyzed for insertion/generation of markers asdescribed in further detail below. In some embodiments, the system 200 amay be primed with a first/initial set of training data 214 a that maysubsequently be supplemented with additional data as set forth below.

The classification processor 218 a may process the sample frames 210 a(potentially in conjunction with the training data 214 a) to generate aclassification 222 a of the content item/video. The classification 222 amay serve to identify one or more characteristics of the contentitem/video as represented by the sample frames 210 a. For example, theclassification 222 a may serve to identify a genre/category of thecontent item/video, characters in the video, actors/actresses appearingin the video, events or conditions associated with an environmentcaptured in the video, emotions/sentiments expressed in the video,rhythms or patterns in terms of scene or segment transitions in thevideo, etc., or any combination thereof. The classification 222 a may beincorporated as part of the training data 214 a to facilitate futureiterations/executions of the system 200 a.

Referring now to FIG. 2B, a block diagram is shown illustrating anexample, non-limiting embodiment of a system 200 b functioning withinthe communication network 100 of FIG. 1 in accordance with variousaspects described herein. The system 200 b may be utilized as part of atraining algorithm/routine to generate classifications of one or moreportions of content/content items, such as for example audio. Thoseclassifications may be used for establishing/generating markers for aninsertion of one or more creatives as described in further detail below.While shown separately, in some embodiments one or more aspects of thesystem 200 a may be combined with one or more aspects of the system 200b.

In operation, the system 200 b may obtain as input audio data 202 b. Theaudio data 202 b may correspond to audio generated by a productionstudio. In some embodiments, the audio data 202 b may be sourced orobtained from a user equipment (e.g., a mobile phone). In someembodiments, the audio data 202 b may correspond to or include audioassociated with a streaming or over-the-top (OTT) video. In someembodiments, the audio data 202 b may be uploaded to one or more devices(e.g., one or more servers) to facilitate a subsequent download to oneor more devices. In some embodiments, the audio data 202 b maycorrespond to live content, such as for example audio contentdistributed over one or more platforms (e.g., one or more social mediaplatforms).

The audio data 202 b may be provided to a processor, such as for examplea sampling processor 206 b as shown in FIG. 2B (in some embodiments, thesampling processor 206 b may correspond to the sampling processor 206 aof FIG. 2A). The sampling processor 206 b may sample the audio data 202b to generate audio samples 210 b. In some embodiments, a sample ratethat is used by the sampling processor 206 b to sample the audio data202 b may be based on an identification of a content item (e.g., avideo, a musical track or compilation, speech, etc.) associated with theaudio data 202 b. For example, if the content item is known to becomplex (e.g., features a multitude of different sounds, pitches,volumes, etc.), then a high sampling rate may be used. Conversely, ifthe content item is known to be simplistic (e.g., features relativelymonotone or continuous sounds), a lower sampling rate may be used. Insome embodiments, the sampling rate may be based in part on anavailability of resources. For example, if an amount of processingresources (e.g., resources provided by the sampling processor 206 b)that are available is high, a greater number of audio samples 210 b maybe generated relative to a state/condition where the amount ofprocessing resources that are available is low.

The audio samples 210 b may be provided as an input to a classificationprocessor 218 b (where the classification processor 218 b may correspondto a same entity/device as the sampling processor 206 b in someembodiments and/or where the classification processor 218 b maycorrespond to the classification processor 218 a of FIG. 2A). To theextent that training data 214 b is available, the training data 214 bmay also be provided as an input to the classification processor 218 b.

The training data 214 b may represent a corpus of data that isrepresentative of a cross-section of content items (e.g., audio content)that may be analyzed for insertion/generation of markers as described infurther detail below. In some embodiments, the system 200 b may beprimed with a first/initial set of training data 214 b that maysubsequently be supplemented with additional data as set forth below.

The classification processor 218 b may process the audio samples 210 b(potentially in conjunction with the training data 214 b) to generate aclassification 222 b of the content item. The classification 222 b mayserve to identify one or more characteristics of the content item asrepresented by the audio samples 210 b. For example, the classification222 b may serve to identify a genre/category of the audio, performers orspeakers in the audio, events or conditions associated with anenvironment in which the audio was captured, emotions/sentiments,rhythms or patterns in terms of transitions between segments/sections ofthe audio, etc., or any combination thereof. The classification 222 bmay be incorporated as part of the training data 214 b to facilitatefuture iterations/executions of the system 200 b.

In some embodiments, the classification(s) 222 a and/or theclassification(s) 222 b may be utilized as part of a machine learning(ML) model to classify future instances of data associated with, e.g.,content items. For example, and referring to the system 200 c shown inFIG. 2C, a ML model 214 c may incorporate aspects of theclassification(s) 222 a and/or the classification(s) 222 b. Inoperation, raw data 202 c that may be associated with a content item(e.g., images, video, audio, text, etc.) may be provided as an input toan encoding processor 206 c. The encoding processor 206 c may processthe raw data 202 c to generate one or more samples 210 c. A ML processor218 c may process the samples 210 c using the ML model 214 c to generatea classification 222 c for the content item.

In some embodiments, the classification 222 c may identifycharacteristics associated with the content item, such as for exampleone or more characteristics described above. Based on the classification222 c (or associated characteristics), locations for one or more markers224 c in the content item may be identified (by the ML processor 218 c,or by another device or processor not shown in FIG. 2C). For example,the markers 224 c may be placed at locations within the content item todelineate inventory that is available for creative insertion. Theinventory may correspond to an advertising opportunity, such thatcreatives that are inserted to populate at least a portion of theinventory may include one or more advertisements. The markers 224 c mayreplace pre-existing markers, which is to say that the pre-existingmarkers may be overwritten or deleted/removed. In some embodiments, themarkers 224 c may supplement pre-existing markers, which is to say thatthe markers 224 c may be added to a collection of markers that alsoincludes the pre-existing markers.

In some embodiments, the classification 222 c and/or the markers 224 cmay be provided as input to an error correction processor 226 c. Theerror correction processor 226 c may obtain feedback 230 c asanother/secondary input. The feedback 230 c may beactively/affirmatively obtained, such as for example based on: responsesto user surveys or questionnaires, purchases of products or servicesincluded in or associated with advertisements contained within abounding range of the markers 224 c, etc. The feedback 230 c may bepassively obtained, such as for example based on gaze trackingtechnology of a user, biometric sensor measurements (e.g., heart orpulse rate) associated with the user, user equipment activities (or,analogously, a lack thereof), etc.

The error correction processor 226 c may process the inputs that theprocessor 226 c obtains to generate error correction data 234 c. Theerror correction data 234 c may represent errors that may be present inthe classification 222 c and/or the markers 224 c. The errors/errorcorrection data 234 c may be provided as an input to the ML model 214 c.The ML model 214 c may be modified/adapted based on the error correctiondata 234 c, resulting in a modified/updated model. The modified/updatedmodel may be utilized in future iterations/executions of/by the system200 c. In this respect, as the system 200 c is used, the system 200 c(e.g., the model 214 c) may tend to become more accurate over time, andany errors (as represented by, e.g., the error correction data 234 c)may tend to converge towards zero. This reduction in error mayencourage/incentivize even further/additional use of the system 200 c,which is to say that the utilization/adoption of the system 200 c maygrow/increase (e.g., may grow/increase exponentially).

Referring now to FIG. 2D, an illustrative embodiment of a method 200 din accordance with various aspects described herein is shown. The method200 d may be implemented (e.g., executed), in whole or in part, inconjunction with one or more systems, devices, and/or components, suchas for example the systems, devices, and components set forth herein. Insome embodiments, the method 200 d may be executed to identify andincorporate one or more markers in a first content item (e.g., a mediacontent item). The marker(s) may delineate inventory available withinthe first content item and the inventory may be populated with one ormore creatives. A creative may correspond to or include a second/anothercontent item, such as for example an advertisement.

In block 202 d, a model may be obtained. For example, the model may begenerated as part of one or more training regiments or routines. Thegeneration of the model 202 d may be based on an execution of/by one ormore training systems, such as for example the systems 200 a and 200 bof FIGS. 2A-2B. For example, as part of block 202 d known content itemsrepresented by data may be subjected to training algorithms tobuild-up/establish a corpus of known classifications that may be used inconjunction with the model.

In block 206 d, an unclassified/raw content item may be obtained. Forexample, the unclassified/raw content item may be obtained based on anew studio production becoming available, a user uploading a filecorresponding to the content item, live content becoming available (suchas for example in relation to a streaming distribution model), etc.

In block 210 d, samples of the raw content item of block 210 d may beobtained. A sampling rate associated with the samples may be based onone or more factors or considerations, such as for example in relationto those set forth above.

In block 214 d, the raw content item of block 206 d (or, any samplesassociated therewith obtained as part of block 210 d) may be applied tothe model. The model may compare the raw content item (or the samplesthereof) to the classifications of the model to generate classificationcharacteristics for the raw content item (or samples thereof). As partof block 214 d, and potentially based on the classifications generatedin block 214 d, one or more markers for the content item may begenerated. Locations for the markers may be identified as part of block214 d.

In some embodiments, as part of block 214 d the raw content item may beapplied to the model at different points in time, or on differentoccasions, with different results in terms of the markers that aregenerated. For example, as the model evolves over time, potentially inconjunction with training data, the markers that are identified maychange (e.g., improve) and the marker placement/locations may change(e.g., improve).

In block 218 d, the markers of block 214 d may be inserted into thecontent item at the locations identified as part of block 214 d. In someembodiments, the locations may be based on various factors, such as forexample: an identification of a type of device that is used to presentthe content item, an identification of a physical/geographical locationof the device, an identification of a user preference, etc.

In block 222 d, one or more creatives may be identified to populateinventory delineated by the markers of blocks 214 d and 218 d. Forexample, the creative(s) may be identified based on the use of one ormore bidding models as would be appreciated by one of skill in the art.In some embodiments, creatives may be selected to populate the inventorybased on a user profile. In some embodiments, creatives may be selectedto populate inventory based on a device capability of a communicationdevice (e.g., a suer equipment) that obtains the creatives. In someembodiments, creatives may be selected to populate the inventory basedon classification characteristics of block 214 d. As part of block 222d, the inventory may be populated with the creative(s) that is/areidentified/selected to facilitate a presentation of the creative(s) atthe time/location corresponding to the duration of the markers.

In block 226 d, errors in the classification characteristics and/or themarkers of block 214 d may be identified. As part of block 226 d, themodel (of block 202 d) may be modified, based on the identified errors,to generate a modified model. The modified model may be utilized (aspart of block 214 d) in subsequent executions/iterations of the method200 d.

While for purposes of simplicity of explanation, the respectiveprocesses are shown and described as a series of blocks in FIG. 2D, itis to be understood and appreciated that the claimed subject matter isnot limited by the order of the blocks, as some blocks may occur indifferent orders and/or concurrently with other blocks from what isdepicted and described herein. Moreover, not all illustrated blocks maybe required to implement the methods described herein.

As described above, in some embodiments content (e.g., a raw orunclassified content item of the type referred to in block 206 d of FIG.2D) may correspond to/include live content. Relative to pre-recordedcontent, live content may represent an additional challenge in the sensethat future events, conditions, or occurrences are not necesairly knownin advance, such that a determination of where to place a marker withinthe live content may be difficult. In this respect, in some embodimentslive content that is obtained may be buffered before being presented bya user equipment. An analysis of the buffered content may be performedto dynamically select and insert markers into the buffered content. Inthis respect, the content that is presented by the user equipment mightnot be instantaneous or in real-time relative to when the content iscaptured; e.g., a presentation of content may be subjected tobuffering/delay to facilitate intelligence in terms of decision-makingprocesses regarding marker insertion. Tradeoffs may be made between theextent/size of the delay/buffering relative to a need or desire topresent the live content in real-time.

As set forth herein, aspects of this disclosure may utilize machinelearning and artificial intelligence technologies to identify locationswithin a content item to insert markers. The markers may delineateinventory that may be populated with one or more creatives. In someembodiments, the markers may be implemented as metadata that mayaccompany data associated with the content item. In some embodiments, amarker may represent/include first characteristics of a first portion ofthe content item that precedes the marker and/or second characteristicsof a second portion of the content item that follows the marker. Themarkers/metadata may be used to identify a creative (e.g., anadvertisement) to be presented to a user.

In some embodiments, creatives may be populated within a content item ona first device (e.g., a server), and the combination of the content itemand the creatives may be transmitted to a second device (e.g., a userequipment) for presentation at/by the second device. In someembodiments, the population of a content item with creatives may beperformed at a device that also presents the content item.

In some embodiments, respective locations of inventory in a content itemidentified/delineated by markers may be different for different devices.This may be true, even if the same content item is being provided to thedifferent devices. In this respect, a different playback experience maybe obtained by a second user of a second device relative to a first userof a first device.

In some embodiments, a communication device (e.g., a user equipment) mayrequest a content item. Based on that request, data associated with thecontent item may be applied to a (version of a) model toidentify/determine/generate classification characteristics and/ormarkers.

As one skilled in the art would appreciate based on a review of thisdisclosure, various aspects of this disclosure represent improvements toconventional technology in terms of the selection, placement, anddistribution of one or more creatives. Rather than utilizing a one-sizefits-all approach to the treatment of creatives, aspects of thisdisclosure are transformative in nature and adapt the treatment ofcreatives to the particular circumstances, events, or conditions thatmay be at hand at a given point in time. Aspects of this disclosure maybe tied to particular/specific apparatuses, machines or devices that maybe programmed to perform one or more of the methodological acts setforth herein.

Referring now to FIG. 3 , a block diagram 300 is shown illustrating anexample, non-limiting embodiment of a virtualized communication networkin accordance with various aspects described herein. In particular avirtualized communication network is presented that can be used toimplement some or all of the subsystems and functions of system 100, thesubsystems and functions of systems 200 a-200 c, and method 200 dpresented in FIGS. 1 and 2A-2D. For example, virtualized communicationnetwork 300 can facilitate in whole or in part obtaining a model,obtaining first data corresponding to a first content item, sampling thefirst data to obtain first samples of the first data, applying the firstsamples to the model to generate first classification characteristics,analyzing the first classification characteristics to generate a firstplurality of markers, wherein each of the first plurality of markersdelineates respective inventory within the first content item, selectingcreatives to populate the inventory within the first content item,resulting in first selected creatives, and causing the inventory withinthe first content item to be populated with the first selectedcreatives. Virtualized communication network 300 can facilitate in wholeor in part obtaining first data corresponding to a first content item,applying the first data to a model to generate first classificationcharacteristics, analyzing the first classification characteristics togenerate a first plurality of markers, wherein each of the firstplurality of markers delineates respective first locations of inventorywithin the first content item, selecting creatives to populate theinventory within the first content item, resulting in first selectedcreatives, and causing the inventory within the first content item to bepopulated with the first selected creatives. Virtualized communicationnetwork 300 can facilitate in whole or in part applying first dataassociated with a first content item to a model to generate firstclassification characteristics, analyzing the first classificationcharacteristics to generate a first marker, wherein the first markerdelineates a first location of inventory within the first content item,selecting a first creative to populate a portion of the inventory, andpopulating, based on the selecting, the inventory with the firstcreative.

In particular, a cloud networking architecture is shown that leveragescloud technologies and supports rapid innovation and scalability via atransport layer 350, a virtualized network function cloud 325 and/or oneor more cloud computing environments 375. In various embodiments, thiscloud networking architecture is an open architecture that leveragesapplication programming interfaces (APIs); reduces complexity fromservices and operations; supports more nimble business models; andrapidly and seamlessly scales to meet evolving customer requirementsincluding traffic growth, diversity of traffic types, and diversity ofperformance and reliability expectations.

In contrast to traditional network elements—which are typicallyintegrated to perform a single function, the virtualized communicationnetwork employs virtual network elements (VNEs) 330, 332, 334, etc. thatperform some or all of the functions of network elements 150, 152, 154,156, etc. For example, the network architecture can provide a substrateof networking capability, often called Network Function VirtualizationInfrastructure (NFVI) or simply infrastructure that is capable of beingdirected with software and Software Defined Networking (SDN) protocolsto perform a broad variety of network functions and services. Thisinfrastructure can include several types of substrates. The most typicaltype of substrate being servers that support Network FunctionVirtualization (NFV), followed by packet forwarding capabilities basedon generic computing resources, with specialized network technologiesbrought to bear when general purpose processors or general purposeintegrated circuit devices offered by merchants (referred to herein asmerchant silicon) are not appropriate. In this case, communicationservices can be implemented as cloud-centric workloads.

As an example, a traditional network element 150 (shown in FIG. 1 ),such as an edge router can be implemented via a VNE 330 composed of NFVsoftware modules, merchant silicon, and associated controllers. Thesoftware can be written so that increasing workload consumes incrementalresources from a common resource pool, and moreover so that it'selastic: so the resources are only consumed when needed. In a similarfashion, other network elements such as other routers, switches, edgecaches, and middle-boxes are instantiated from the common resource pool.Such sharing of infrastructure across a broad set of uses makes planningand growing infrastructure easier to manage.

In an embodiment, the transport layer 350 includes fiber, cable, wiredand/or wireless transport elements, network elements and interfaces toprovide broadband access 110, wireless access 120, voice access 130,media access 140 and/or access to content sources 175 for distributionof content to any or all of the access technologies. In particular, insome cases a network element needs to be positioned at a specific place,and this allows for less sharing of common infrastructure. Other times,the network elements have specific physical layer adapters that cannotbe abstracted or virtualized, and might require special DSP code andanalog front-ends (AFEs) that do not lend themselves to implementationas VNEs 330, 332 or 334. These network elements can be included intransport layer 350.

The virtualized network function cloud 325 interfaces with the transportlayer 350 to provide the VNEs 330, 332, 334, etc. to provide specificNFVs. In particular, the virtualized network function cloud 325leverages cloud operations, applications, and architectures to supportnetworking workloads. The virtualized network elements 330, 332 and 334can employ network function software that provides either a one-for-onemapping of traditional network element function or alternately somecombination of network functions designed for cloud computing. Forexample, VNEs 330, 332 and 334 can include route reflectors, domain namesystem (DNS) servers, and dynamic host configuration protocol (DHCP)servers, system architecture evolution (SAE) and/or mobility managemententity (MME) gateways, broadband network gateways, IP edge routers forIP-VPN, Ethernet and other services, load balancers, distributers andother network elements. Because these elements don't typically need toforward large amounts of traffic, their workload can be distributedacross a number of servers—each of which adds a portion of thecapability, and overall which creates an elastic function with higheravailability than its former monolithic version. These virtual networkelements 330, 332, 334, etc. can be instantiated and managed using anorchestration approach similar to those used in cloud compute services.

The cloud computing environments 375 can interface with the virtualizednetwork function cloud 325 via APIs that expose functional capabilitiesof the VNEs 330, 332, 334, etc. to provide the flexible and expandedcapabilities to the virtualized network function cloud 325. Inparticular, network workloads may have applications distributed acrossthe virtualized network function cloud 325 and cloud computingenvironment 375 and in the commercial cloud, or might simply orchestrateworkloads supported entirely in NFV infrastructure from these thirdparty locations.

Turning now to FIG. 4 , there is illustrated a block diagram of acomputing environment in accordance with various aspects describedherein. In order to provide additional context for various embodimentsof the embodiments described herein, FIG. 4 and the following discussionare intended to provide a brief, general description of a suitablecomputing environment 400 in which the various embodiments of thesubject disclosure can be implemented. In particular, computingenvironment 400 can be used in the implementation of network elements150, 152, 154, 156, access terminal 112, base station or access point122, switching device 132, media terminal 142, and/or VNEs 330, 332,334, etc. Each of these devices can be implemented viacomputer-executable instructions that can run on one or more computers,and/or in combination with other program modules and/or as a combinationof hardware and software. For example, computing environment 400 canfacilitate in whole or in part obtaining a model, obtaining first datacorresponding to a first content item, sampling the first data to obtainfirst samples of the first data, applying the first samples to the modelto generate first classification characteristics, analyzing the firstclassification characteristics to generate a first plurality of markers,wherein each of the first plurality of markers delineates respectiveinventory within the first content item, selecting creatives to populatethe inventory within the first content item, resulting in first selectedcreatives, and causing the inventory within the first content item to bepopulated with the first selected creatives. Computing environment 400can facilitate in whole or in part obtaining first data corresponding toa first content item, applying the first data to a model to generatefirst classification characteristics, analyzing the first classificationcharacteristics to generate a first plurality of markers, wherein eachof the first plurality of markers delineates respective first locationsof inventory within the first content item, selecting creatives topopulate the inventory within the first content item, resulting in firstselected creatives, and causing the inventory within the first contentitem to be populated with the first selected creatives. Computingenvironment 400 can facilitate in whole or in part applying first dataassociated with a first content item to a model to generate firstclassification characteristics, analyzing the first classificationcharacteristics to generate a first marker, wherein the first markerdelineates a first location of inventory within the first content item,selecting a first creative to populate a portion of the inventory, andpopulating, based on the selecting, the inventory with the firstcreative.

Generally, program modules comprise routines, programs, components, datastructures, etc., that perform particular tasks or implement particularabstract data types. Moreover, those skilled in the art will appreciatethat the methods can be practiced with other computer systemconfigurations, comprising single-processor or multiprocessor computersystems, minicomputers, mainframe computers, as well as personalcomputers, hand-held computing devices, microprocessor-based orprogrammable consumer electronics, and the like, each of which can beoperatively coupled to one or more associated devices.

As used herein, a processing circuit includes one or more processors aswell as other application specific circuits such as an applicationspecific integrated circuit, digital logic circuit, state machine,programmable gate array or other circuit that processes input signals ordata and that produces output signals or data in response thereto. Itshould be noted that while any functions and features described hereinin association with the operation of a processor could likewise beperformed by a processing circuit.

The illustrated embodiments of the embodiments herein can be alsopracticed in distributed computing environments where certain tasks areperformed by remote processing devices that are linked through acommunications network. In a distributed computing environment, programmodules can be located in both local and remote memory storage devices.

Computing devices typically comprise a variety of media, which cancomprise computer-readable storage media and/or communications media,which two terms are used herein differently from one another as follows.Computer-readable storage media can be any available storage media thatcan be accessed by the computer and comprises both volatile andnonvolatile media, removable and non-removable media. By way of example,and not limitation, computer-readable storage media can be implementedin connection with any method or technology for storage of informationsuch as computer-readable instructions, program modules, structured dataor unstructured data.

Computer-readable storage media can comprise, but are not limited to,random access memory (RAM), read only memory (ROM), electricallyerasable programmable read only memory (EEPROM),flash memory or othermemory technology, compact disk read only memory (CD-ROM), digitalversatile disk (DVD) or other optical disk storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devicesor other tangible and/or non-transitory media which can be used to storedesired information. In this regard, the terms “tangible” or“non-transitory” herein as applied to storage, memory orcomputer-readable media, are to be understood to exclude onlypropagating transitory signals per se as modifiers and do not relinquishrights to all standard storage, memory or computer-readable media thatare not only propagating transitory signals per se.

Computer-readable storage media can be accessed by one or more local orremote computing devices, e.g., via access requests, queries or otherdata retrieval protocols, for a variety of operations with respect tothe information stored by the medium.

Communications media typically embody computer-readable instructions,data structures, program modules or other structured or unstructureddata in a data signal such as a modulated data signal, e.g., a carrierwave or other transport mechanism, and comprises any informationdelivery or transport media. The term “modulated data signal” or signalsrefers to a signal that has one or more of its characteristics set orchanged in such a manner as to encode information in one or moresignals. By way of example, and not limitation, communication mediacomprise wired media, such as a wired network or direct-wiredconnection, and wireless media such as acoustic, RF, infrared and otherwireless media.

With reference again to FIG. 4 , the example environment can comprise acomputer 402, the computer 402 comprising a processing unit 404, asystem memory 406 and a system bus 408. The system bus 408 couplessystem components including, but not limited to, the system memory 406to the processing unit 404. The processing unit 404 can be any ofvarious commercially available processors. Dual microprocessors andother multiprocessor architectures can also be employed as theprocessing unit 404.

The system bus 408 can be any of several types of bus structure that canfurther interconnect to a memory bus (with or without a memorycontroller), a peripheral bus, and a local bus using any of a variety ofcommercially available bus architectures. The system memory 406comprises ROM 410 and RAM 412. A basic input/output system (BIOS) can bestored in a non-volatile memory such as ROM, erasable programmable readonly memory (EPROM), EEPROM, which BIOS contains the basic routines thathelp to transfer information between elements within the computer 402,such as during startup. The RAM 412 can also comprise a high-speed RAMsuch as static RAM for caching data.

The computer 402 further comprises an internal hard disk drive (HDD) 414(e.g., EIDE, SATA), which internal HDD 414 can also be configured forexternal use in a suitable chassis (not shown), a magnetic floppy diskdrive (FDD) 416, (e.g., to read from or write to a removable diskette418) and an optical disk drive 420, (e.g., reading a CD-ROM disk 422 or,to read from or write to other high capacity optical media such as theDVD). The HDD 414, magnetic FDD 416 and optical disk drive 420 can beconnected to the system bus 408 by a hard disk drive interface 424, amagnetic disk drive interface 426 and an optical drive interface 428,respectively. The hard disk drive interface 424 for external driveimplementations comprises at least one or both of Universal Serial Bus(USB) and Institute of Electrical and Electronics Engineers (IEEE) 1394interface technologies. Other external drive connection technologies arewithin contemplation of the embodiments described herein.

The drives and their associated computer-readable storage media providenonvolatile storage of data, data structures, computer-executableinstructions, and so forth. For the computer 402, the drives and storagemedia accommodate the storage of any data in a suitable digital format.Although the description of computer-readable storage media above refersto a hard disk drive (HDD), a removable magnetic diskette, and aremovable optical media such as a CD or DVD, it should be appreciated bythose skilled in the art that other types of storage media which arereadable by a computer, such as zip drives, magnetic cassettes, flashmemory cards, cartridges, and the like, can also be used in the exampleoperating environment, and further, that any such storage media cancontain computer-executable instructions for performing the methodsdescribed herein.

A number of program modules can be stored in the drives and RAM 412,comprising an operating system 430, one or more application programs432, other program modules 434 and program data 436. All or portions ofthe operating system, applications, modules, and/or data can also becached in the RAM 412. The systems and methods described herein can beimplemented utilizing various commercially available operating systemsor combinations of operating systems.

A user can enter commands and information into the computer 402 throughone or more wired/wireless input devices, e.g., a keyboard 438 and apointing device, such as a mouse 440. Other input devices (not shown)can comprise a microphone, an infrared (IR) remote control, a joystick,a game pad, a stylus pen, touch screen or the like. These and otherinput devices are often connected to the processing unit 404 through aninput device interface 442 that can be coupled to the system bus 408,but can be connected by other interfaces, such as a parallel port, anIEEE 1394 serial port, a game port, a universal serial bus (USB) port,an IR interface, etc.

A monitor 444 or other type of display device can be also connected tothe system bus 408 via an interface, such as a video adapter 446. Itwill also be appreciated that in alternative embodiments, a monitor 444can also be any display device (e.g., another computer having a display,a smart phone, a tablet computer, etc.) for receiving displayinformation associated with computer 402 via any communication means,including via the Internet and cloud-based networks. In addition to themonitor 444, a computer typically comprises other peripheral outputdevices (not shown), such as speakers, printers, etc.

The computer 402 can operate in a networked environment using logicalconnections via wired and/or wireless communications to one or moreremote computers, such as a remote computer(s) 448. The remotecomputer(s) 448 can be a workstation, a server computer, a router, apersonal computer, portable computer, microprocessor-based entertainmentappliance, a peer device or other common network node, and typicallycomprises many or all of the elements described relative to the computer402, although, for purposes of brevity, only a remote memory/storagedevice 450 is illustrated. The logical connections depicted comprisewired/wireless connectivity to a local area network (LAN) 452 and/orlarger networks, e.g., a wide area network (WAN) 454. Such LAN and WANnetworking environments are commonplace in offices and companies, andfacilitate enterprise-wide computer networks, such as intranets, all ofwhich can connect to a global communications network, e.g., theInternet.

When used in a LAN networking environment, the computer 402 can beconnected to the LAN 452 through a wired and/or wireless communicationnetwork interface or adapter 456. The adapter 456 can facilitate wiredor wireless communication to the LAN 452, which can also comprise awireless AP disposed thereon for communicating with the adapter 456.

When used in a WAN networking environment, the computer 402 can comprisea modem 458 or can be connected to a communications server on the WAN454 or has other means for establishing communications over the WAN 454,such as by way of the Internet. The modem 458, which can be internal orexternal and a wired or wireless device, can be connected to the systembus 408 via the input device interface 442. In a networked environment,program modules depicted relative to the computer 402 or portionsthereof, can be stored in the remote memory/storage device 450. It willbe appreciated that the network connections shown are example and othermeans of establishing a communications link between the computers can beused.

The computer 402 can be operable to communicate with any wirelessdevices or entities operatively disposed in wireless communication,e.g., a printer, scanner, desktop and/or portable computer, portabledata assistant, communications satellite, any piece of equipment orlocation associated with a wirelessly detectable tag (e.g., a kiosk,news stand, restroom), and telephone. This can comprise WirelessFidelity (Wi-Fi) and BLUETOOTH® wireless technologies. Thus, thecommunication can be a predefined structure as with a conventionalnetwork or simply an ad hoc communication between at least two devices.

Wi-Fi can allow connection to the Internet from a couch at home, a bedin a hotel room or a conference room at work, without wires. Wi-Fi is awireless technology similar to that used in a cell phone that enablessuch devices, e.g., computers, to send and receive data indoors and out;anywhere within the range of a base station. Wi-Fi networks use radiotechnologies called IEEE 802.11 (a, b, g, n, ac, ag, etc.) to providesecure, reliable, fast wireless connectivity. A Wi-Fi network can beused to connect computers to each other, to the Internet, and to wirednetworks (which can use IEEE 802.3 or Ethernet). Wi-Fi networks operatein the unlicensed 2.4 and 5 GHz radio bands for example or with productsthat contain both bands (dual band), so the networks can providereal-world performance similar to the basic 10BaseT wired Ethernetnetworks used in many offices.

Turning now to FIG. 5 , an embodiment 500 of a mobile network platform510 is shown that is an example of network elements 150, 152, 154, 156,and/or VNEs 330, 332, 334, etc. For example, platform 510 can facilitatein whole or in part obtaining a model, obtaining first datacorresponding to a first content item, sampling the first data to obtainfirst samples of the first data, applying the first samples to the modelto generate first classification characteristics, analyzing the firstclassification characteristics to generate a first plurality of markers,wherein each of the first plurality of markers delineates respectiveinventory within the first content item, selecting creatives to populatethe inventory within the first content item, resulting in first selectedcreatives, and causing the inventory within the first content item to bepopulated with the first selected creatives. Platform 510 can facilitatein whole or in part obtaining first data corresponding to a firstcontent item, applying the first data to a model to generate firstclassification characteristics, analyzing the first classificationcharacteristics to generate a first plurality of markers, wherein eachof the first plurality of markers delineates respective first locationsof inventory within the first content item, selecting creatives topopulate the inventory within the first content item, resulting in firstselected creatives, and causing the inventory within the first contentitem to be populated with the first selected creatives. Platform 510 canfacilitate in whole or in part applying first data associated with afirst content item to a model to generate first classificationcharacteristics, analyzing the first classification characteristics togenerate a first marker, wherein the first marker delineates a firstlocation of inventory within the first content item, selecting a firstcreative to populate a portion of the inventory, and populating, basedon the selecting, the inventory with the first creative.

In one or more embodiments, the mobile network platform 510 can generateand receive signals transmitted and received by base stations or accesspoints such as base station or access point 122. Generally, mobilenetwork platform 510 can comprise components, e.g., nodes, gateways,interfaces, servers, or disparate platforms, that facilitate bothpacket-switched (PS) (e.g., internet protocol (IP), frame relay,asynchronous transfer mode (ATM)) and circuit-switched (CS) traffic(e.g., voice and data), as well as control generation for networkedwireless telecommunication. As a non-limiting example, mobile networkplatform 510 can be included in telecommunications carrier networks, andcan be considered carrier-side components as discussed elsewhere herein.Mobile network platform 510 comprises CS gateway node(s) 512 which caninterface CS traffic received from legacy networks like telephonynetwork(s) 540 (e.g., public switched telephone network (PSTN), orpublic land mobile network (PLMN)) or a signaling system #7 (SS7)network 560. CS gateway node(s) 512 can authorize and authenticatetraffic (e.g., voice) arising from such networks. Additionally, CSgateway node(s) 512 can access mobility, or roaming, data generatedthrough SS7 network 560; for instance, mobility data stored in a visitedlocation register (VLR), which can reside in memory 530. Moreover, CSgateway node(s) 512 interfaces CS-based traffic and signaling and PSgateway node(s) 518. As an example, in a 3GPP UMTS network, CS gatewaynode(s) 512 can be realized at least in part in gateway GPRS supportnode(s) (GGSN). It should be appreciated that functionality and specificoperation of CS gateway node(s) 512, PS gateway node(s) 518, and servingnode(s) 516, is provided and dictated by radio technology(ies) utilizedby mobile network platform 510 for telecommunication over a radio accessnetwork 520 with other devices, such as a radiotelephone 575.

In addition to receiving and processing CS-switched traffic andsignaling, PS gateway node(s) 518 can authorize and authenticatePS-based data sessions with served mobile devices. Data sessions cancomprise traffic, or content(s), exchanged with networks external to themobile network platform 510, like wide area network(s) (WANs) 550,enterprise network(s) 570, and service network(s) 580, which can beembodied in local area network(s) (LANs), can also be interfaced withmobile network platform 510 through PS gateway node(s) 518. It is to benoted that WANs 550 and enterprise network(s) 570 can embody, at leastin part, a service network(s) like IP multimedia subsystem (IMS). Basedon radio technology layer(s) available in technology resource(s) orradio access network 520, PS gateway node(s) 518 can generate packetdata protocol contexts when a data session is established; other datastructures that facilitate routing of packetized data also can begenerated. To that end, in an aspect, PS gateway node(s) 518 cancomprise a tunnel interface (e.g., tunnel termination gateway (TTG) in3GPP UMTS network(s) (not shown)) which can facilitate packetizedcommunication with disparate wireless network(s), such as Wi-Finetworks.

In embodiment 500, mobile network platform 510 also comprises servingnode(s) 516 that, based upon available radio technology layer(s) withintechnology resource(s) in the radio access network 520, convey thevarious packetized flows of data streams received through PS gatewaynode(s) 518. It is to be noted that for technology resource(s) that relyprimarily on CS communication, server node(s) can deliver trafficwithout reliance on PS gateway node(s) 518; for example, server node(s)can embody at least in part a mobile switching center. As an example, ina 3GPP UMTS network, serving node(s) 516 can be embodied in serving GPRSsupport node(s) (SGSN).

For radio technologies that exploit packetized communication, server(s)514 in mobile network platform 510 can execute numerous applicationsthat can generate multiple disparate packetized data streams or flows,and manage (e.g., schedule, queue, format . . . ) such flows. Suchapplication(s) can comprise add-on features to standard services (forexample, provisioning, billing, customer support . . . ) provided bymobile network platform 510. Data streams (e.g., content(s) that arepart of a voice call or data session) can be conveyed to PS gatewaynode(s) 518 for authorization/authentication and initiation of a datasession, and to serving node(s) 516 for communication thereafter. Inaddition to application server, server(s) 514 can comprise utilityserver(s), a utility server can comprise a provisioning server, anoperations and maintenance server, a security server that can implementat least in part a certificate authority and firewalls as well as othersecurity mechanisms, and the like. In an aspect, security server(s)secure communication served through mobile network platform 510 toensure network's operation and data integrity in addition toauthorization and authentication procedures that CS gateway node(s) 512and PS gateway node(s) 518 can enact. Moreover, provisioning server(s)can provision services from external network(s) like networks operatedby a disparate service provider; for instance, WAN 550 or GlobalPositioning System (GPS) network(s) (not shown). Provisioning server(s)can also provision coverage through networks associated to mobilenetwork platform 510 (e.g., deployed and operated by the same serviceprovider), such as the distributed antennas networks shown in FIG. 1(s)that enhance wireless service coverage by providing more networkcoverage.

It is to be noted that server(s) 514 can comprise one or more processorsconfigured to confer at least in part the functionality of mobilenetwork platform 510. To that end, the one or more processor can executecode instructions stored in memory 530, for example. It is should beappreciated that server(s) 514 can comprise a content manager, whichoperates in substantially the same manner as described hereinbefore.

In example embodiment 500, memory 530 can store information related tooperation of mobile network platform 510. Other operational informationcan comprise provisioning information of mobile devices served throughmobile network platform 510, subscriber databases; applicationintelligence, pricing schemes, e.g., promotional rates, flat-rateprograms, couponing campaigns; technical specification(s) consistentwith telecommunication protocols for operation of disparate radio, orwireless, technology layers; and so forth. Memory 530 can also storeinformation from at least one of telephony network(s) 540, WAN 550, SS7network 560, or enterprise network(s) 570. In an aspect, memory 530 canbe, for example, accessed as part of a data store component or as aremotely connected memory store.

In order to provide a context for the various aspects of the disclosedsubject matter, FIG. 5 , and the following discussion, are intended toprovide a brief, general description of a suitable environment in whichthe various aspects of the disclosed subject matter can be implemented.While the subject matter has been described above in the general contextof computer-executable instructions of a computer program that runs on acomputer and/or computers, those skilled in the art will recognize thatthe disclosed subject matter also can be implemented in combination withother program modules. Generally, program modules comprise routines,programs, components, data structures, etc. that perform particulartasks and/or implement particular abstract data types.

Turning now to FIG. 6 , an illustrative embodiment of a communicationdevice 600 is shown. The communication device 600 can serve as anillustrative embodiment of devices such as data terminals 114, mobiledevices 124, vehicle 126, display devices 144 or other client devicesfor communication via either communications network 125. For example,computing device 600 can facilitate in whole or in part obtaining amodel, obtaining first data corresponding to a first content item,sampling the first data to obtain first samples of the first data,applying the first samples to the model to generate first classificationcharacteristics, analyzing the first classification characteristics togenerate a first plurality of markers, wherein each of the firstplurality of markers delineates respective inventory within the firstcontent item, selecting creatives to populate the inventory within thefirst content item, resulting in first selected creatives, and causingthe inventory within the first content item to be populated with thefirst selected creatives. Computing device 600 can facilitate in wholeor in part obtaining first data corresponding to a first content item,applying the first data to a model to generate first classificationcharacteristics, analyzing the first classification characteristics togenerate a first plurality of markers, wherein each of the firstplurality of markers delineates respective first locations of inventorywithin the first content item, selecting creatives to populate theinventory within the first content item, resulting in first selectedcreatives, and causing the inventory within the first content item to bepopulated with the first selected creatives. Computing device 600 canfacilitate in whole or in part applying first data associated with afirst content item to a model to generate first classificationcharacteristics, analyzing the first classification characteristics togenerate a first marker, wherein the first marker delineates a firstlocation of inventory within the first content item, selecting a firstcreative to populate a portion of the inventory, and populating, basedon the selecting, the inventory with the first creative.

The communication device 600 can comprise a wireline and/or wirelesstransceiver 602 (herein transceiver 602), a user interface (UI) 604, apower supply 614, a location receiver 616, a motion sensor 618, anorientation sensor 620, and a controller 606 for managing operationsthereof. The transceiver 602 can support short-range or long-rangewireless access technologies such as Bluetooth®, ZigBee®, WiFi, DECT, orcellular communication technologies, just to mention a few (Bluetooth®and ZigBee® are trademarks registered by the Bluetooth® Special InterestGroup and the ZigBee® Alliance, respectively). Cellular technologies caninclude, for example, CDMA-1X, UMTS/HSDPA, GSM/GPRS, TDMA/EDGE, EV/DO,WiMAX, SDR, LTE, as well as other next generation wireless communicationtechnologies as they arise. The transceiver 602 can also be adapted tosupport circuit-switched wireline access technologies (such as PSTN),packet-switched wireline access technologies (such as TCP/IP, VoIP,etc.), and combinations thereof.

The UI 604 can include a depressible or touch-sensitive keypad 608 witha navigation mechanism such as a roller ball, a joystick, a mouse, or anavigation disk for manipulating operations of the communication device600. The keypad 608 can be an integral part of a housing assembly of thecommunication device 600 or an independent device operably coupledthereto by a tethered wireline interface (such as a USB cable) or awireless interface supporting for example Bluetooth®. The keypad 608 canrepresent a numeric keypad commonly used by phones, and/or a QWERTYkeypad with alphanumeric keys. The UI 604 can further include a display610 such as monochrome or color LCD (Liquid Crystal Display), OLED(Organic Light Emitting Diode) or other suitable display technology forconveying images to an end user of the communication device 600. In anembodiment where the display 610 is touch-sensitive, a portion or all ofthe keypad 608 can be presented by way of the display 610 withnavigation features.

The display 610 can use touch screen technology to also serve as a userinterface for detecting user input. As a touch screen display, thecommunication device 600 can be adapted to present a user interfacehaving graphical user interface (GUI) elements that can be selected by auser with a touch of a finger. The display 610 can be equipped withcapacitive, resistive or other forms of sensing technology to detect howmuch surface area of a user's finger has been placed on a portion of thetouch screen display. This sensing information can be used to controlthe manipulation of the GUI elements or other functions of the userinterface. The display 610 can be an integral part of the housingassembly of the communication device 600 or an independent devicecommunicatively coupled thereto by a tethered wireline interface (suchas a cable) or a wireless interface.

The UI 604 can also include an audio system 612 that utilizes audiotechnology for conveying low volume audio (such as audio heard inproximity of a human ear) and high volume audio (such as speakerphonefor hands free operation). The audio system 612 can further include amicrophone for receiving audible signals of an end user. The audiosystem 612 can also be used for voice recognition applications. The UI604 can further include an image sensor 613 such as a charged coupleddevice (CCD) camera for capturing still or moving images.

The power supply 614 can utilize common power management technologiessuch as replaceable and rechargeable batteries, supply regulationtechnologies, and/or charging system technologies for supplying energyto the components of the communication device 600 to facilitatelong-range or short-range portable communications. Alternatively, or incombination, the charging system can utilize external power sources suchas DC power supplied over a physical interface such as a USB port orother suitable tethering technologies.

The location receiver 616 can utilize location technology such as aglobal positioning system (GPS) receiver capable of assisted GPS foridentifying a location of the communication device 600 based on signalsgenerated by a constellation of GPS satellites, which can be used forfacilitating location services such as navigation. The motion sensor 618can utilize motion sensing technology such as an accelerometer, agyroscope, or other suitable motion sensing technology to detect motionof the communication device 600 in three-dimensional space. Theorientation sensor 620 can utilize orientation sensing technology suchas a magnetometer to detect the orientation of the communication device600 (north, south, west, and east, as well as combined orientations indegrees, minutes, or other suitable orientation metrics).

The communication device 600 can use the transceiver 602 to alsodetermine a proximity to a cellular, WiFi, Bluetooth®, or other wirelessaccess points by sensing techniques such as utilizing a received signalstrength indicator (RSSI) and/or signal time of arrival (TOA) or time offlight (TOF) measurements. The controller 606 can utilize computingtechnologies such as a microprocessor, a digital signal processor (DSP),programmable gate arrays, application specific integrated circuits,and/or a video processor with associated storage memory such as Flash,ROM, RAM, SRAM, DRAM or other storage technologies for executingcomputer instructions, controlling, and processing data supplied by theaforementioned components of the communication device 600.

Other components not shown in FIG. 6 can be used in one or moreembodiments of the subject disclosure. For instance, the communicationdevice 600 can include a slot for adding or removing an identity modulesuch as a Subscriber Identity Module (SIM) card or Universal IntegratedCircuit Card (UICC). SIM or UICC cards can be used for identifyingsubscriber services, executing programs, storing subscriber data, and soon.

The terms “first,” “second,” “third,” and so forth, as used in theclaims, unless otherwise clear by context, is for clarity only anddoesn't otherwise indicate or imply any order in time. For instance, “afirst determination,” “a second determination,” and “a thirddetermination,” does not indicate or imply that the first determinationis to be made before the second determination, or vice versa, etc.

In the subject specification, terms such as “store,” “storage,” “datastore,” data storage,” “database,” and substantially any otherinformation storage component relevant to operation and functionality ofa component, refer to “memory components,” or entities embodied in a“memory” or components comprising the memory. It will be appreciatedthat the memory components described herein can be either volatilememory or nonvolatile memory, or can comprise both volatile andnonvolatile memory, by way of illustration, and not limitation, volatilememory, non-volatile memory, disk storage, and memory storage. Further,nonvolatile memory can be included in read only memory (ROM),programmable ROM (PROM), electrically programmable ROM (EPROM),electrically erasable ROM (EEPROM), or flash memory. Volatile memory cancomprise random access memory (RAM), which acts as external cachememory. By way of illustration and not limitation, RAM is available inmany forms such as synchronous RAM (SRAM), dynamic RAM (DRAM),synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhancedSDRAM (ESDRAM), Synchlink DRAM (SLDRAM), and direct Rambus RAM(DRRAIVI). Additionally, the disclosed memory components of systems ormethods herein are intended to comprise, without being limited tocomprising, these and any other suitable types of memory.

Moreover, it will be noted that the disclosed subject matter can bepracticed with other computer system configurations, comprisingsingle-processor or multiprocessor computer systems, mini-computingdevices, mainframe computers, as well as personal computers, hand-heldcomputing devices (e.g., PDA, phone, smartphone, watch, tabletcomputers, netbook computers, etc.), microprocessor-based orprogrammable consumer or industrial electronics, and the like. Theillustrated aspects can also be practiced in distributed computingenvironments where tasks are performed by remote processing devices thatare linked through a communications network; however, some if not allaspects of the subject disclosure can be practiced on stand-alonecomputers. In a distributed computing environment, program modules canbe located in both local and remote memory storage devices.

In one or more embodiments, information regarding use of services can begenerated including services being accessed, media consumption history,user preferences, and so forth. This information can be obtained byvarious methods including user input, detecting types of communications(e.g., video content vs. audio content), analysis of content streams,sampling, and so forth. The generating, obtaining and/or monitoring ofthis information can be responsive to an authorization provided by theuser. In one or more embodiments, an analysis of data can be subject toauthorization from user(s) associated with the data, such as an opt-in,an opt-out, acknowledgement requirements, notifications, selectiveauthorization based on types of data, and so forth.

Some of the embodiments described herein can also employ artificialintelligence (AI) to facilitate automating one or more featuresdescribed herein. The embodiments (e.g., in connection withautomatically identifying acquired cell sites that provide a maximumvalue/benefit after addition to an existing communication network) canemploy various AI-based schemes for carrying out various embodimentsthereof. Moreover, the classifier can be employed to determine a rankingor priority of each cell site of the acquired network. A classifier is afunction that maps an input attribute vector, x=(x1, x2, x3, x4, . . . ,xn), to a confidence that the input belongs to a class, that is,f(x)=confidence (class). Such classification can employ a probabilisticand/or statistical-based analysis (e.g., factoring into the analysisutilities and costs) to determine or infer an action that a user desiresto be automatically performed. A support vector machine (SVM) is anexample of a classifier that can be employed. The SVM operates byfinding a hypersurface in the space of possible inputs, which thehypersurface attempts to split the triggering criteria from thenon-triggering events. Intuitively, this makes the classificationcorrect for testing data that is near, but not identical to trainingdata. Other directed and undirected model classification approachescomprise, e.g., naïve Bayes, Bayesian networks, decision trees, neuralnetworks, fuzzy logic models, and probabilistic classification modelsproviding different patterns of independence can be employed.Classification as used herein also is inclusive of statisticalregression that is utilized to develop models of priority.

As will be readily appreciated, one or more of the embodiments canemploy classifiers that are explicitly trained (e.g., via a generictraining data) as well as implicitly trained (e.g., via observing UEbehavior, operator preferences, historical information, receivingextrinsic information). For example, SVMs can be configured via alearning or training phase within a classifier constructor and featureselection module. Thus, the classifier(s) can be used to automaticallylearn and perform a number of functions, including but not limited todetermining according to predetermined criteria which of the acquiredcell sites will benefit a maximum number of subscribers and/or which ofthe acquired cell sites will add minimum value to the existingcommunication network coverage, etc.

As used in some contexts in this application, in some embodiments, theterms “component,” “system” and the like are intended to refer to, orcomprise, a computer-related entity or an entity related to anoperational apparatus with one or more specific functionalities, whereinthe entity can be either hardware, a combination of hardware andsoftware, software, or software in execution. As an example, a componentmay be, but is not limited to being, a process running on a processor, aprocessor, an object, an executable, a thread of execution,computer-executable instructions, a program, and/or a computer. By wayof illustration and not limitation, both an application running on aserver and the server can be a component. One or more components mayreside within a process and/or thread of execution and a component maybe localized on one computer and/or distributed between two or morecomputers. In addition, these components can execute from variouscomputer readable media having various data structures stored thereon.The components may communicate via local and/or remote processes such asin accordance with a signal having one or more data packets (e.g., datafrom one component interacting with another component in a local system,distributed system, and/or across a network such as the Internet withother systems via the signal). As another example, a component can be anapparatus with specific functionality provided by mechanical partsoperated by electric or electronic circuitry, which is operated by asoftware or firmware application executed by a processor, wherein theprocessor can be internal or external to the apparatus and executes atleast a part of the software or firmware application. As yet anotherexample, a component can be an apparatus that provides specificfunctionality through electronic components without mechanical parts,the electronic components can comprise a processor therein to executesoftware or firmware that confers at least in part the functionality ofthe electronic components. While various components have beenillustrated as separate components, it will be appreciated that multiplecomponents can be implemented as a single component, or a singlecomponent can be implemented as multiple components, without departingfrom example embodiments.

Further, the various embodiments can be implemented as a method,apparatus or article of manufacture using standard programming and/orengineering techniques to produce software, firmware, hardware or anycombination thereof to control a computer to implement the disclosedsubject matter. The term “article of manufacture” as used herein isintended to encompass a computer program accessible from anycomputer-readable device or computer-readable storage/communicationsmedia. For example, computer readable storage media can include, but arenot limited to, magnetic storage devices (e.g., hard disk, floppy disk,magnetic strips), optical disks (e.g., compact disk (CD), digitalversatile disk (DVD)), smart cards, and flash memory devices (e.g.,card, stick, key drive). Of course, those skilled in the art willrecognize many modifications can be made to this configuration withoutdeparting from the scope or spirit of the various embodiments.

In addition, the words “example” and “exemplary” are used herein to meanserving as an instance or illustration. Any embodiment or designdescribed herein as “example” or “exemplary” is not necessarily to beconstrued as preferred or advantageous over other embodiments ordesigns. Rather, use of the word example or exemplary is intended topresent concepts in a concrete fashion. As used in this application, theterm “or” is intended to mean an inclusive “or” rather than an exclusive“or”. That is, unless specified otherwise or clear from context, “Xemploys A or B” is intended to mean any of the natural inclusivepermutations. That is, if X employs A; X employs B; or X employs both Aand B, then “X employs A or B” is satisfied under any of the foregoinginstances. In addition, the articles “a” and “an” as used in thisapplication and the appended claims should generally be construed tomean “one or more” unless specified otherwise or clear from context tobe directed to a singular form.

Moreover, terms such as “user equipment,” “mobile station,” “mobile,”subscriber station,” “access terminal,” “terminal,” “handset,” “mobiledevice” (and/or terms representing similar terminology) can refer to awireless device utilized by a subscriber or user of a wirelesscommunication service to receive or convey data, control, voice, video,sound, gaming or substantially any data-stream or signaling-stream. Theforegoing terms are utilized interchangeably herein and with referenceto the related drawings.

Furthermore, the terms “user,” “subscriber,” “customer,” “consumer” andthe like are employed interchangeably throughout, unless contextwarrants particular distinctions among the terms. It should beappreciated that such terms can refer to human entities or automatedcomponents supported through artificial intelligence (e.g., a capacityto make inference based, at least, on complex mathematical formalisms),which can provide simulated vision, sound recognition and so forth.

As employed herein, the term “processor” can refer to substantially anycomputing processing unit or device comprising, but not limited tocomprising, single-core processors; single-processors with softwaremultithread execution capability; multi-core processors; multi-coreprocessors with software multithread execution capability; multi-coreprocessors with hardware multithread technology; parallel platforms; andparallel platforms with distributed shared memory. Additionally, aprocessor can refer to an integrated circuit, an application specificintegrated circuit (ASIC), a digital signal processor (DSP), a fieldprogrammable gate array (FPGA), a programmable logic controller (PLC), acomplex programmable logic device (CPLD), a discrete gate or transistorlogic, discrete hardware components or any combination thereof designedto perform the functions described herein. Processors can exploitnano-scale architectures such as, but not limited to, molecular andquantum-dot based transistors, switches and gates, in order to optimizespace usage or enhance performance of user equipment. A processor canalso be implemented as a combination of computing processing units.

As used herein, terms such as “data storage,” data storage,” “database,”and substantially any other information storage component relevant tooperation and functionality of a component, refer to “memorycomponents,” or entities embodied in a “memory” or components comprisingthe memory. It will be appreciated that the memory components orcomputer-readable storage media, described herein can be either volatilememory or nonvolatile memory or can include both volatile andnonvolatile memory.

What has been described above includes mere examples of variousembodiments. It is, of course, not possible to describe everyconceivable combination of components or methodologies for purposes ofdescribing these examples, but one of ordinary skill in the art canrecognize that many further combinations and permutations of the presentembodiments are possible. Accordingly, the embodiments disclosed and/orclaimed herein are intended to embrace all such alterations,modifications and variations that fall within the spirit and scope ofthe appended claims. Furthermore, to the extent that the term “includes”is used in either the detailed description or the claims, such term isintended to be inclusive in a manner similar to the term “comprising” as“comprising” is interpreted when employed as a transitional word in aclaim.

In addition, a flow diagram may include a “start” and/or “continue”indication. The “start” and “continue” indications reflect that thesteps presented can optionally be incorporated in or otherwise used inconjunction with other routines. In this context, “start” indicates thebeginning of the first step presented and may be preceded by otheractivities not specifically shown. Further, the “continue” indicationreflects that the steps presented may be performed multiple times and/ormay be succeeded by other activities not specifically shown. Further,while a flow diagram indicates a particular ordering of steps, otherorderings are likewise possible provided that the principles ofcausality are maintained.

As may also be used herein, the term(s) “operably coupled to”, “coupledto”, and/or “coupling” includes direct coupling between items and/orindirect coupling between items via one or more intervening items. Suchitems and intervening items include, but are not limited to, junctions,communication paths, components, circuit elements, circuits, functionalblocks, and/or devices. As an example of indirect coupling, a signalconveyed from a first item to a second item may be modified by one ormore intervening items by modifying the form, nature or format ofinformation in a signal, while one or more elements of the informationin the signal are nevertheless conveyed in a manner than can berecognized by the second item. In a further example of indirectcoupling, an action in a first item can cause a reaction on the seconditem, as a result of actions and/or reactions in one or more interveningitems.

Although specific embodiments have been illustrated and describedherein, it should be appreciated that any arrangement which achieves thesame or similar purpose may be substituted for the embodiments describedor shown by the subject disclosure. The subject disclosure is intendedto cover any and all adaptations or variations of various embodiments.Combinations of the above embodiments, and other embodiments notspecifically described herein, can be used in the subject disclosure.For instance, one or more features from one or more embodiments can becombined with one or more features of one or more other embodiments. Inone or more embodiments, features that are positively recited can alsobe negatively recited and excluded from the embodiment with or withoutreplacement by another structural and/or functional feature. The stepsor functions described with respect to the embodiments of the subjectdisclosure can be performed in any order. The steps or functionsdescribed with respect to the embodiments of the subject disclosure canbe performed alone or in combination with other steps or functions ofthe subject disclosure, as well as from other embodiments or from othersteps that have not been described in the subject disclosure. Further,more than or less than all of the features described with respect to anembodiment can also be utilized.

What is claimed is:
 1. A device, comprising: a processing systemincluding a processor; and a memory that stores executable instructionsthat, when executed by the processing system, facilitate a performanceof operations, the operations comprising: obtaining first datacorresponding to a first content item; applying the first data to amodel to generate first classification characteristics; analyzing thefirst classification characteristics to generate a first marker, whereinthe first maker delineates first inventory within the first contentitem; selecting a first creative to populate the first inventory,resulting in a first selected creative; causing the first inventory tobe populated with the first selected creative; subsequent to the causingof the first inventory to be populated with the first selected creative,identifying an error in the first classification characteristics, thefirst marker, or a combination thereof; and modifying the mode based onthe identifying of the error, resulting in a modified model.
 2. Thedevice of claim 1, wherein the operations further comprise: obtainingfeedback subsequent to the causing of the first inventory to bepopulated with the first selected creative, wherein the identifying ofthe error is based on the feedback.
 3. The device of claim 1, whereinthe operations further comprise: obtaining second data corresponding toa second content item; applying the second data to the modified model togenerate second classification characteristics; analyzing the secondclassification characteristics to generate a second marker, wherein thesecond marker delineates second inventory within the second contentitem; selecting a second creative to populate the second inventory,resulting a second selected creative; and causing the second inventoryto be populated with the second selected creative.
 4. The device ofclaim 1, wherein the operations further comprise: transmitting the firstmarker as metadata to a user equipment.
 5. The device of claim 4,wherein the causing of the first inventory to be populated with thefirst selected creative comprises transmitting the first selectedcreative to the user equipment to cause the user equipment to populatethe first inventory with the first selected creative at a locationidentified by the first marker.
 6. The device of claim 1, wherein theoperations further comprise: obtaining second data corresponding to asecond content item; sampling the second data to obtain second samplesof the second data; applying the second samples to the modified model togenerate second classification characteristics; analyzing the secondclassification characteristics to generate at least a second marker,wherein each marker of the at least a second marker delineatesrespective inventory within the second content item; selecting at leastone creative to populate the inventory within the second content item,resulting in an at least a second selected creative; and causing theinventory within the second content item to be populated with the atleast a second selected creative.
 7. The device of claim 1, wherein theselecting of the first creative is based on a user profile associatedwith a user of a user equipment, and wherein the user equipment obtainsthe first selected creative.
 8. The device of claim 1, wherein theselecting of the first creative is based on a device capability of auser equipment that obtains the first selected creative.
 9. The deviceof claim 1, wherein the obtaining of the first data comprises obtainingsecond data from a user equipment.
 10. The device of claim 9, whereinthe obtaining of the second data is via a social media platform.
 11. Thedevice of claim 9, wherein the obtaining of the second data comprisesobtaining the second data as live streaming content.
 12. The device ofclaim 11, wherein the operations further comprise: buffering the livestreaming content to generate the first data.
 13. The device of claim 1,wherein the first data comprises image data.
 14. The device of claim 1,wherein the first data comprises audio data.
 15. The device of claim 1,wherein the operations further comprise: generating the model based on aprocessing of a plurality of identified content items in accordance withtraining data.
 16. A non-transitory machine-readable medium, comprisingexecutable instructions that, when executed by a processing systemincluding a processor, facilitate a performance of operations, theoperations comprising: applying first data to a model to generate firstclassification characteristics; analyzing the first classificationcharacteristics to generate a first plurality of markers, wherein eachof the first plurality of markers delineates respective first locationsof inventory within a first content item; selecting at least onecreative to populate the inventory within the first content item,resulting in an at least one first selective creative; and causing theinventory within the first content item to be populated with the atleast one first selective creative.
 17. The non-transitorymachine-readable medium of claim 16, wherein the at least one firstselective creative comprises a plurality of creatives.
 18. Thenon-transitory machine-readable medium of claim 16, wherein the firstcontent item comprises a first video, and wherein the firstclassification characteristics identify: a genre of the first video,characters in the first video, actors or actresses appearing in thefirst video, events or conditions associated with an environmentcaptured in the first video, emotions or sentiments expressed in thefirst video, and rhythms or patterns in terms of scene or segmenttransitions in the first video.
 19. A method, comprising: analyzing, bya processing system including a processor, first classificationcharacteristics to generate a first marker, wherein the first markerdelineates a first location of first inventory within a first contentitem; selecting, by the processing system, a first creative to populatea portion of the first inventory; and populating, by the processingsystem and based on the selecting, the portion of the first inventorywith the first creative.
 20. The method of claim 19, further comprising:receiving, by the processing system, a request for the first contentitem from a user equipment; based on the receiving of the request,generating, by the processing system, second classificationcharacteristics that are at least partially differentiated from thefirst classification characteristics; analyzing, by the processingsystem, the second classification characteristics to generate a secondmarker, wherein the second marker delineates a second location of secondinventory within the first content item, and wherein the second locationis different from the first location; and populating, by the processingsystem and for the user equipment, the second inventory with the firstcreative and a second creative.